# NetworkCNNLayer¶

class NetworkCNNLayer.CNN(n_features=1, filter=1, d_row=-1, border_mode='valid', conv_stride=(1, 1), pool_size=(1, 1), filter_dilation=(1, 1), ignore_border=1, pool_stride=0, pool_padding=(0, 0), mode='max', activation='tanh', dropout=0.0, factor=1.0, base=None, transpose=False, force_sample=False, **kwargs)[source]
Parameters: n_features (int) – integer the number of feature map(s), e.g. 32, 64, or so on. the input will be interpret as (width|time, batch, height * n_in_features) and the output will be (width|time, batch, height * n_features). filter (int|(int,int)) – integer or tuple of length 2 the filter size/shape, i.e. the number of row(s) and/or columns(s) from the filter shape. when this filter type is integer, it means the number of rows the same as the number of columns. e.g. 3, 5, (1,3), or so on. d_row (int) – integer the number of row(s) from the input the default value is -1, which the dimension comes from the n_out of the input. otherwise, this has to be filled only for the first convolutional layer and the rest layer will use the number of rows from the previous layer. border_mode (str) – string “valid” – only apply filter to complete patches of the image. Generates output of shape: (image_shape - filter_shape + 1). ”full” – zero-pads image to multiple of filter shape to generate output of shape: (image_shape + filter_shape - 1). “same” – keep the dimension of convolutional layer output the same as the input dimension. conv_stride ((int,int)) – tuple of length 2 factor by which to subsample the convolutional layer output. this stride is writen in (rows,columns). pool_size ((int,int)) – tuple of length 2 factor by which to downscale in pooling layer. this is written in (rows,columns). the default value is (2,2), it will halve the input in each dimension. filter_dilation ((int,int)) – tuple of length 2 factor by which to subsample (stride) the convolutional layer input. ignore_border (int|bool) – integer or boolean 1 or True – (5, 5) input with pool_size = (2, 2), will generate a (2, 2) pooling layer output. 0 or False – (5, 5) input with pool_size = (2, 2), will generate a (3, 3) pooling layer output. pool_stride ((int,int)) – tuple of length 2 stride size, which is the number of shifts over rows/cols to get the next pool region. the default value is 0, it will set equal to pool_size, which means no overlap on pooling regions. pool_padding ((int,int)) – tuple of length 2 pad zeros to extend beyond four borders of the images. this is writen in (pad_h,pad_w), where pad_h is the size of the top and bottom margins, and pad_w is the size of the left and right margins. mode (str) – string pooling layer mode that excludes the padding in the computation. “max” – max pooling “sum” – sum pooling “avg” – average pooling “fmp” – fractional max pooling activation (str) – string activation function, e.g. “tanh”, “sigmoid”, “relu”, “elu”, “maxout”, and so on. factor (float) – float factor by which scale the initial weights
recurrent = True[source]
get_status(sources)[source]
get_dim(input, filters, pools, border_mode, stride, pool_stride, ignore_border, pad)[source]
calculate_index(inputs)[source]
calculate_dropout(dropout, inputs)[source]
convolution(inputs, filter_shape, stride, border_mode, factor, pool_size, filter_dilation)[source]
pooling(inputs, pool_size, ignore_border, stride, pad, mode)[source]
bias_term(inputs, n_features, activation)[source]
run_cnn(inputs, filter_shape, filter_dilation, params, modes, others)[source]
class NetworkCNNLayer.NewConv(**kwargs)[source]
layer_class = 'conv'[source]

this class is for standard CNN and inception

class NetworkCNNLayer.ConcatConv(padding=False, **kwargs)[source]
layer_class = 'conv_1d'[source]

this class is for the CNN that processes an entire line image as the input by concatenated several frames by time axis.

class NetworkCNNLayer.ResNet(**kwargs)[source]
layer_class = 'resnet'[source]

this class is for resnet connection.